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Research Machine Learning Federated Learning Jobs

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... The research intern will be in a fast-paced start-up environment playing a crucial technical role ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... The research intern will be in a fast-paced start-up environment playing a crucial technical role ...

OpenAI

Research Engineer, Privacy

San Francisco, CA · On-site

$380K - $445K/yr

... machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning ... Have hands-on research or production experience with PETs. * Are fluent in modern deep-learning ...

Avride

Machine Learning Engineer

Austin, TX

Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Research, analyze, support, and implement machine learning solutions on the Snowflake Cloud data warehouse platform using the Snowpark framework Develop novel solutions using knowledge of the latest ...

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$146K - $222K/yr

We have an opening for Machine Learning Research experts to join our team and advance the discipline as well as apply cutting edge tools and techniques to some of society's most important problems.

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$9.8K/wk

We have an opening for Machine Learning Research experts to join our team and advance the discipline as well as apply cutting edge tools and techniques to some of society's most important problems.

LLNL

Machine Learning Research Engineer

Livermore, CA

$146K - $222K/yr

We have an opening for Machine Learning Research experts to join our team and advance the discipline as well as apply cutting edge tools and techniques to some of society's most important problems.

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All JobsResearch Machine Learning Federated Learning Jobs

Research Machine Learning Federated Learning information

See salary details

$25.5K

$42.6K

$88K

How much do research machine learning federated learning jobs pay per year?

As of Jul 22, 2026, the average yearly pay for research machine learning federated learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a Researcher in Machine Learning Federated Learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

More about Research Machine Learning Federated Learning jobs
What cities are hiring for Research Machine Learning Federated Learning jobs? Cities with the most Research Machine Learning Federated Learning job openings:
What states have the most Research Machine Learning Federated Learning jobs? States with the most job openings for Research Machine Learning Federated Learning jobs include:
What job categories do people searching Research Machine Learning Federated Learning jobs look for? The top searched job categories for Research Machine Learning Federated Learning jobs are:
Research Machine Learning Federated Learning jobs near you
Infographic showing various Research Machine Learning Federated Learning job openings in the United States as of July 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer

Institute of Foundation Models

Sunnyvale, CA • On-site

$150K - $450K/yr

Full-time

Posted 8 days ago

Job description

About the Institute of Foundation Models
We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.



The Role
As a Machine Learning Engineer at the Institute of Foundation Models, your primary responsibility is to develop and implement innovative machine learning models that address real-world challenges, pushing the boundaries of artificial intelligence research. You will collaborate with cross-functional teams to deploy scalable solutions, contributing to MBZUAI’s mission of driving impactful AI discoveries and positioning the institution as a leader in the global AI research community. Your expertise will be key in enhancing the performance of large-scale machine learning models, while supporting the development of transformative AI tools that can influence industries worldwide.
Key Responsibilites
  • Collaborate with Research teams to understand technologies, adapting and integrating them into codebase.
  • Develop and implement systems to support the lifecycle of machine learning models, such as data preprocessing, pre-training, post-training, evaluation and so on, especially foundation models.
  • Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  • Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  • Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  • Contribute to research papers and represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
  • Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Academic Qualifications
  • Minimum: Bachelor’s degree or equivalent practical experience. 
  • Preferred: Master's degree or PhD in Computer Science or related technical field.
 
Professional Experience - Minimum
  • 3 years of experience in software engineering, including experience with Machine Learning (ML) models, ML infrastructure, Natural Language Processing or Computer Vision.
  • 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting.
  • 2 years of experience with data structures or algorithms in either an academic or industry setting.
  • 2 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, or natural language processing.
  • Excellent problem-solving and troubleshooting skills to address complex technical challenges.
  • Effective communication and collaboration skills to work with cross functional teams.
Professional Experience - Preferred
  • 2 years of experience with improving performance during large scale data processing
  • Hands-on experience with LLM algorithms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF).
  • Excellent data analysis skills.
Visa Sponsorship
This position is eligible for visa sponsorship.

Benefits Include
*Comprehensive medical, dental, and vision benefits 
 *Bonus
*401K Plan
*Generous paid time off, sick leave and holidays
*Paid Parental Leave
*Employee Assistance Program
*Life insurance and disability